DOCSLIB.ORG

2Nd Workshop on Building and Evaluating Resources for Biomedical Text Mining

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
2Nd Workshop on Building and Evaluating Resources for Biomedical Text Mining 2nd Workshop on Building and Evaluating Resources for Biomedical Text Mining Tuesday, 18th March 2010 Valletta, Malta Organisers: Sophia Ananiadou Kevin Cohen Dina Demner-Fushman Workshop Programme 9:15 – 9:30 Welcome 9:30 – 10:30 Invited Talk (chair: Sophia Ananiadou) Pierre Zweigenbaum, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI-CNRS), France 10:30 – 11:00 Coffee break 11:00 – 12:30 Session 1 (chair: Kevin Cohen) 11:00 Spelling Correction in Clinical Notes with Emphasis on First Suggestion Accuracy Jon Patrick, Mojtaba Sabbagh, Suvir Jain and Haifeng Zheng 11:25 Automatically Building a Repository to Support Evidence Based Practice Dina Demner-Fushman, Joanna Karpinski and George Thoma 11:50 An Empirical Evaluation of Resources for the Identif cation of Diseases and Adverse Effects in Biomedical Literature Harsha Gurulingappa, Roman Klinger, Martin Hofmann-Apitius and Juliane Fluck 12:15 A Task-Oriented Extension of Chinese MeSH Concepts Hierarchy Xinkai Wang and Sophia Ananiadou 12:40 – 14:10 Lunch break 14:10 – 15:10 Invited talk (chair: Sophia Ananiadou) Simonetta Montemagni, Istituto di Linguistica Computazionale (ILC-CNR), Italy 15:10 – 16:55 Session 2 (chair: Dina Demner-Fushman) 15:10 Structuring of Status Descriptions in Hospital Patient Records Svetla Boytcheva, Ivelina Nikolova, Elena Paskaleva, Galia Angelova, Dimitar Tcharaktchiev and Nadya Dimitrova 15:35 Annotation of All Coreference in Biomedical Text: Guideline Selection and Adaptation K. Bretonnel Cohen, Arrick Lanfranchi, William Corvey, William A. Baumgartner Jr., Christophe Roeder, Philip V. Ogren, Martha Palmer and Lawrence Hunter 16:00 – 16:30 Coffee break 16:30 Contribution of Syntactic Structures to Single Word Term Extraction Xing Zhang and Alex Chengyu Fang 16:55 – 17:10 Concluding remarks (chair: Sophia Ananiadou) i Workshop Organisers Sophia Ananiadou, NaCTeM, University of Manchester, UK Kevin Cohen, Center for Computational Pharmacology and The MITRE Corporation, USA Dina Demner-Fushman, National Library of Medicine, USA Workshop Programme Committee Olivier Bodenreider, National Library of Medicine, USA Wendy Chapman, University of Pittsburgh, USA Aaron Cohen, Oregon Health and Science University Liu Hong Fang, Georgetown University Medical Center, USA Martin Krallinger, National Biotechnology Center, Spain John McNaught, NaCTeM, University of Manchester, UK John Pestian, Computational Medicine Center, University of Cincinnati, USA Andrey Rzhetsky, University of Chicago, USA Jian Su, Institute for Infocomm Research, Singapore Junichi Tsujii, University of Tokyo, Japan and NaCTeM, University of Manchester, UK Yoshimasa Tsuruoka, JAIST, Japan Karin Verspoor, Center for Computational Pharmacology, University of Colorado, USA Xinglong Wang, University of Manchester, UK Bonnie Webber, University of Edinburgh, UK John Wilbur, NCBI, NLM, NIH, USA Pierre Zweigenbaum, LIMSI-CNRS, France ii Table of Contents Foreword Sophia Ananiadou, Kevin Cohen and Dina Demner Fushman ………………... 1 Spelling Correction in Clinical Notes with Emphasis on First Suggestion Accuracy Jon Patrick, Mojtaba Sabbagh, Suvir Jain and Haifeng Zheng ……………….. 2 Automatically Building a Repository to Support Evidence Based Practice Dina Demner-Fushman, Joanna Karpinski and George Thoma ………………. 9 An Empirical Evaluation of Resources for the Identif cation of Diseases and Adverse Effects in Biomedical Literature Harsha Gurulingappa, Roman Klinger, Martin Hofmann-Apitius and Juliane Fluck ………………………………………………………………. 15 A Task-Oriented Extension of the Chinese MeSH Concepts Hierarchy Xinkai Wang and Sophia Ananiadou …………………………………………… 23 Structuring of Status Descriptions in Hospital Patient Records Svetla Boytcheva, Ivelina Nikolova, Elena Paskaleva, Galia Angelova, Dimitar Tcharaktchiev and Nadya Dimitrova …………………………………. 31 Annotation of All Coreference in Biomedical Text: Guideline Selection and Adaptation K. Bretonnel Cohen, Arrick Lanfranchi, William Corvey, William A. Baumgartner Jr., Christophe Roeder, Philip V. Ogren, Martha Palmer and Lawrence Hunter …………………………………………. 37 Contribution of Syntactic Structures to Single Word Term Extraction Xing Zhang and Alex Chengyu Fang …………………………………………… 42 iii Author Index Sophia Ananiadou, 23 Galia Angelova, 31 William A. Baumgartner Jr., 37 Svetla Boytcheva, 31 K. Bretonnel Cohen, 37 William Corvey, 37 Dina Demner-Fushman, 9 Nadya Dimitrova , 31 Alex Chengyu Fang, 42 Juliane Fluck, 15 Harsha Gurulingappa, 15 Martin Hofmann-Apitius, 15 Lawrence Hunter, 37 Suvir Jain, 2 Joanna Karpinski, 9 Roman Klinger, 15 Arrick Lanfranchi, 37 Ivelina Nikolova, 31 Philip V. Ogren, 37 Martha Palmer, 37 Elena Paskaleva, 31 Jon Patrick, 2 Christophe Roeder, 37 Mojtaba Sabbagh, 2 Dimitar Tcharaktchiev, 31 George Thoma, 9 Xinkai Wang, 23 Xing Zhang, 42 Haifeng Zheng, 2 iv FOREWORD This volum e contains the pap ers accepted at the 2nd w orkshop on Building and Evaluating Resources for Biomedical Text Mining held at LREC 2010, Malta. Biomedical text mining over the last decade has become one of the d riving application areas for the NLP community, resulting in a series of very successful yearly specialist wo rkshops at ACL since 2002, BioNLP, as well as the launch of the BioMed special interest group in 2008. In the past, most of the work has focused on solving specific problems, often using task-tailored and private data sets. This data was rarely reused, in particular outside the efforts of the providers. This has changed during the last years, as many research groups have made available resources that have been built either purposely or as by-products of research or ev aluation efforts. A num ber of projects, initiatives and organisations have been dedicated to building and providing biomedical text mining resources (e.g., the GENIA suite of corpora, PennBioIE, TREC Ge nomics track, BioCreative, Yapex, LLL05, BOOTStrep, JNLPBA, KDD data, Medstract, BioText, etc.). There is an increasing need to provide comm unity-wide discussions on the desi gn, a vailability and interoperability of resources for bio-text m ining, following on from specific applications such as gene name identification and protein-protein inter actions in BioCreative I/II, the BioNLP'09 shared task on event extraction etc., and recognition of clinically relevant entities and relations in the i2b2 challenges to the use of common resources and tools in real life applications. The papers in this volu me reflect the current tran sitional state of the b iomedical text m ining field and range f rom named entity r ecognition in ME DLINE abstracts (Gurulingappa et a l., Zhang and Fang) to reuse of annotation tools and guidelines to fully annotate 97 journal articles (Cohen et al.). The corpus of 97 fully annotated articles will be m ade publicly ava ilable upon com pletion of syntactic, semantic, and discourse structure annotation with the emphasis on coreference annotation. The biom edical text m ining field is expanding in two directions : enrichm ent and use of cross- lingual resources and work in resource-poor languages on the one hand, and significant inroads in processing clinical narrative on the other hand. The enrichm ent and evaluation of cross-lingual resources a re exem plifies in the work on ex tension of the Chinese Medi cal Su bject Headings vocabulary (W ang and Ananiadou). Spanning a re source-poor language and clinical dom ain, Boytcheva et al. focus o n the mining of Bulgarian c linical notes for descriptions of patients’ status and com plications. Patrick et al. focus on spelling errors in clinic al notes and pres ent a detailed description of a spelling suggestion generator. Clin ical notes are linked to MEDLINE citations in the collection under construction at NIH, as described by Demner-Fushman et al. We wish to thank the authors f or subm itting papers f or considera tion, and the mem bers of th e program committee for their time and effort during the review process. We would also like to thank our invited speakers, Simonetta Montemagni and Pierre Zweigenbaum for their contribution. Sophia Ananiadou, Kevin Cohen and Dina Demner-Fushman 1 Spelling correction in Clinical Notes with Emphasis on First Suggestion Accuracy Jon Patrick, Mojtaba Sabbagh, Suvir Jain, Haifeng Zheng Health Information Technology Research Laboratory School of IT, University of Sydney [email protected], [email protected], [email protected], [email protected] Abstract Spelling correction in a large collection of clinical notes is a relatively lesser explored research area and serious problem as typically 30% of tokens are unknown. In this paper we explore techniques to devise a spelling suggestion generator for clinical data with an emphasis on the accuracy of the first suggestion. We use a combination of a rule-based suggestion generation system and a context-sensitive ranking algorithm to obtain the best results. The ranking is achieved using a language model created by bootstrapping from the corpus. The described method is embedded in a practical work-flow that we use for Knowledge Discovery and Reuse in clinical records. 1. Introduction cludes the following resources: Our work specializes in processing corpora of medical texts 1. Systematised Nomenclature of Medicine-Clinical ((Wang and Patrick, 2008), (Ryan et al., 2008), (Zhang and Terms, i.e., SNOMED-CT: 99860 words Patrick, 2007) and (Asgari and Patrick, 2008)). These cor- pora usually contain many years of patient records from 2. The Moby Lexicon: 354992 words hospital
Load more

Recommended publications
  • BMC Bioinformatics Biomed Central
    BMC Bioinformatics BioMed Central Introduction Open Access Proceedings of the Second International Symposium for Semantic Mining in Biomedicine Sophia Ananiadou*1 and Juliane Fluck*2 Address: 1School of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Oxford Road, M13 9PL, Manchester, UK and 2Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 St. Augustin, Germany Email: Sophia Ananiadou* - [email protected]; Juliane Fluck* - [email protected] * Corresponding authors from Second International Symposium on Semantic Mining in Biomedicine (SMBM) Jena, Germany. 9–12 April 2006 Published: 24 November 2006 <supplement> <title> <p>Second International Symposium on Semantic Mining in Biomedicine (SMBM)</p> </title> <editor>Sophia Ananiadou, Juliane Fluck</editor> <note>Proceedings</note> </supplement> BMC Bioinformatics 2006, 7(Suppl 3):S1 doi:10.1186/1471-2105-7-S3-S1 © 2006 Ananiadou and Fluck; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Introduction • discovery of semantic relations between entities With an overwhelming amount of biomedical knowledge recorded in texts, it is not surprising that there is so much • event discovery interest in techniques which can identify, extract, manage, integrate and exploit this knowledge, and moreover dis- The current limitations of using existing terminological cover new, hidden or unsuspected knowledge. For this and ontological resources such as the Gene Ontology, reason, in the past five years, there has been an upsurge of Swiss-Prot, Entrez Gene, UMLS, and Mesh etc.
    [Show full text]
  • Alliheedi Mohammed.Pdf (7.910Mb)
    Procedurally Rhetorical Verb-Centric Frame Semantics as a Knowledge Representation for Argumentation Analysis of Biochemistry Articles by Mohammed Alliheedi A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Computer Science Waterloo, Ontario, Canada, 2019 c Mohammed Alliheedi 2019 Examining Committee Membership External Examiner: Vlado Keselj Professor, Faculty of Computer Science Dalhousie University Supervisor(s): Robert E. Mercer Professor, Dept. of Computer Science, The University of Western Ontario Robin Cohen Professor, School of Computer Science, University of Waterloo Internal Member: Jesse Hoey Associate Professor, School of Computer Science, University of Waterloo Internal-External Member: Randy Harris Professor, Dept. of of English Language and Literature, University of Waterloo Other Member(s): Charles Clarke Professor, School of Computer Science, University of Waterloo ii I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. iii Abstract The central focus of this thesis is rhetorical moves in biochemistry articles. Kanoksila- patham has provided a descriptive theory of rhetorical moves that extends Swales' CARS model to the complete biochemistry article. The thesis begins the construction of a com- putational model of this descriptive theory. Attention is placed on the Methods section of the articles. We hypothesize that because authors' argumentation closely follows their experimental procedure, procedural verbs may be the guide to understanding the rhetor- ical moves. Our work proposes an extension to the normal (i.e., VerbNet) semantic roles especially tuned to this domain.
    [Show full text]
  • Syntactic Analyses and Named Entity Recognition for Pubmed and Pubmed Central — Up-To-The-Minute
    Syntactic analyses and named entity recognition for PubMed and PubMed Central — up-to-the-minute Kai Hakala1;2∗, Suwisa Kaewphan1;2;3∗, Tapio Salakoski1;3 and Filip Ginter1 1. Dept. of Information Technology, University of Turku, Finland 2. The University of Turku Graduate School (UTUGS), University of Turku, Finland 3. Turku Centre for Computer Science (TUCS), Finland [email protected], [email protected], [email protected], [email protected] Abstract Various community efforts, mainly in the form of shared tasks, have resulted in steady improve- Although advanced text mining methods ment in biomedical text mining methods (Kim et specifically adapted to the biomedical do- al., 2009; Segura Bedmar et al., 2013). For in- main are continuously being developed, stance the GENIA shared tasks focusing on ex- their applications on large scale have been tracting biological events, such as gene regula- scarce. One of the main reasons for this tions, have consistently gathered wide interest and is the lack of computational resources and have led to the development of several text mining workforce required for processing large tools (Miwa et al., 2012; Bjorne¨ and Salakoski, text corpora. 2013). These methods have been also succes- In this paper we present a publicly avail- fully applied on a large scale and several biomed- able resource distributing preprocessed ical text mining databases are publicly available biomedical literature including sentence (Van Landeghem et al., 2013a; Franceschini et al., splitting, tokenization, part-of-speech tag- 2013; Muller¨ et al., 2004). Although these re- ging, syntactic parses and named entity sources exist, their number does not reflect the recognition.
    [Show full text]
  • Adding Value to Scholarly Communications Through Text Mining
    Adding Value to Scholarly Communications Enhancing User Experience of Scholarly Communicationthrough through Text Text Mining Mining Sophia Ananiadou UK National Centre for Text Mining • first national text mining centre in the world www.nactem.ac.uk • Remit : Provision of text mining services to support UK research • Funded by • University of Manchester, collaboration with Tokyo From Text to Knowledge Applications, users and techniques Scholarly Communication Requirements • What is needed in the repositories – Annotation and curation assistance • Creation of metadata, consistent manner – Name authorities • Merging and mapping existing resources • Prediction lists based on named entity recognition • Disambiguation – Semantic metadata creation and enhancement Provision of semantic metadata to support search • Extraction of terms and named entities (names of people, organisations, diseases, genes, etc) • Discovery of concepts allows semantic annotation and enrichment of documents – Improves information access by going beyond index terms, enabling semantic querying – Improves clustering, classification of documents • Going a step further: extracting relationships, events from text – Enables even more advanced semantic applications Semantic metadata for whom? Semantic metadata for whom? • end users – adds value to library content – allows enhanced searching functionalities – allows interaction with content, living document • automated content aggregators – access to data-driven, quality metadata derived from text • librarians – enhanced capability for semantic indexing, cross- referencing between Library collections and classification Terminology Services TerMine Identifies the most significant terms Used as metadata Suggests similar areas of interest Refines index terms for document classification Used for ontology building (Protégé TerMine plug-in) Semantic metadata: terms Term Based Applications Tag Cloud based on terms automatically extracted from the blog of BBSRC Chief Executive Professor Kell.
    [Show full text]
  • Syntactic Analyses and Named Entity Recognition for Pubmed and Pubmed Central — Up-To-The-Minute
    Syntactic analyses and named entity recognition for PubMed and PubMed Central — up-to-the-minute 1,2 1,2,3 1,3 1 Kai Hakala ∗, Suwisa Kaewphan ∗, Tapio Salakoski and Filip Ginter 1. Dept. of Information Technology, University of Turku, Finland 2. The University of Turku Graduate School (UTUGS), University of Turku, Finland 3. Turku Centre for Computer Science (TUCS), Finland [email protected], [email protected], [email protected], [email protected] Abstract Various community efforts, mainly in the form of shared tasks, have resulted in steady improve- Although advanced text mining methods ment in biomedical text mining methods (Kim et specifically adapted to the biomedical do- al., 2009; Segura Bedmar et al., 2013). For in- main are continuously being developed, stance the GENIA shared tasks focusing on ex- their applications on large scale have been tracting biological events, such as gene regula- scarce. One of the main reasons for this tions, have consistently gathered wide interest and is the lack of computational resources and have led to the development of several text mining workforce required for processing large tools (Miwa et al., 2012; Bjorne¨ and Salakoski, text corpora. 2013). These methods have been also succes- In this paper we present a publicly avail- fully applied on a large scale and several biomed- able resource distributing preprocessed ical text mining databases are publicly available biomedical literature including sentence (Van Landeghem et al., 2013a; Franceschini et al., splitting, tokenization, part-of-speech tag- 2013; Muller¨ et al., 2004). Although these re- ging, syntactic parses and named entity sources exist, their number does not reflect the recognition.
    [Show full text]
  • Text Mining for Biomedicine an Overview: Selected Bibliography
    Text Mining for Biomedicine an Overview: selected bibliography Sophia Ananiadou a & Yoshimasa Tsuruoka b University of Manchester a, University of Tokyo b National Centre for Text Mining a,b http://www.nactem.ac.uk/ (i) Overviews on Text Mining for Biomedicine Ananiadou, S. & J. McNaught (eds) (2006) Text Mining for Biology and Biomedicine, Artech House. MacMullen, W.J, and S.O. Denn, “Information Problems in Molecular Biology and Bioinformatics,” Journal of the American Society for Information Science and Technology , Vol. 56, No. 5, 2005, pp. 447--456. Lars Juhl Jensen, J. Saric and P. Bork (2006) "Literature mining for the biologist: from information retrieval to biological discovery", In Nature Reviews Genetics, Vol. 7, Feb. 2006, pp 119-129 Blaschke, C., L. Hirschman, and A. Valencia, “Information Extraction in Molecular Biology,” Briefings in Bioinformatics , Vol. 3, No. 2, 2002, pp. 1--12. Cohen, A. M., and W. R. Hersh, “A Survey of Current Work in Biomedical Text Mining,” Briefings in Bioinformatics , Vol. 6, 2005, pp. 57--71. Nédellec, C., “Machine Learning for Information Extraction in Genomics---State of the Art and Perspectives.” In Text Mining and its Applications , pp. 99--118, S. Sirmakessis (ed.), Berlin: Springer-Verlag, Studies in Fuzziness and Soft Computing 138, 2004. Rebholz-Schuhmann, D., H. Kirsch, and F. Couto, “Facts from Text—Is Text Mining Ready to Deliver? ” PLoS Biology , Vol. 3, No. 2, 2005, pp. 0188--0191, http://www.plosbiology.org, June 2005. Shatkay, H., and R. Feldman, “Mining the Biomedical Literature in the Genomic Era: An Overview,” Journal of Computational Biology , Vol. 10, No. 6, 2004, pp.
    [Show full text]
  • Themes in Biomedical Natural Language Processing: Bionlp08
    Edinburgh Research Explorer Themes in biomedical natural language processing: BioNLP08 Citation for published version: Demner-Fushman, D, Ananiadou, S, Cohen, KB, Pestian, J, Tsujii, J & Webber, BL 2008, 'Themes in biomedical natural language processing: BioNLP08', BMC Bioinformatics, vol. 9, no. S-11. https://doi.org/10.1186/1471-2105-9-S11-S1 Digital Object Identifier (DOI): 10.1186/1471-2105-9-S11-S1 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: BMC Bioinformatics General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 29. Sep. 2021 BMC Bioinformatics BioMed Central Research Open Access Themes in biomedical natural language processing: BioNLP08 Dina Demner-Fushman*1, Sophia Ananiadou2, K Bretonnel Cohen3, John Pestian4, Jun'ichi Tsujii5 and Bonnie Webber6 Address: 1US National Library of Medicine, 8600 Rockville Pike, Bethesda,
    [Show full text]
  • Development and Analysis of NLP Pipelines in Argo
    Development and Analysis of NLP Pipelines in Argo Rafal Rak, Andrew Rowley, Jacob Carter, and Sophia Ananiadou National Centre for Text Mining School of Computer Science, University of Manchester Manchester Institute of Biotechnology 131 Princess St, M1 7DN, Manchester, UK rafal.rak,andrew.rowley,jacob.carter,sophia.ananiadou @manchester.ac.uk { } Abstract in text is preceded by text segmentation, part-of- speech recognition, the recognition of named enti- Developing sophisticated NLP pipelines ties, and dependency parsing. Currently, the avail- composed of multiple processing tools ability of such atomic processing components is and components available through differ- no longer an issue; the problem lies in ensur- ent providers may pose a challenge in ing their compatibility, as combining components terms of their interoperability. The Un- coming from multiple repositories, written in dif- structured Information Management Ar- ferent programming languages, requiring different chitecture (UIMA) is an industry stan- installation procedures, and having incompatible dard whose aim is to ensure such in- input/output formats can be a source of frustration teroperability by defining common data and poses a real challenge for developers. structures and interfaces. The architec- Unstructured Information Management Archi- ture has been gaining attention from in- tecture (UIMA) (Ferrucci and Lally, 2004) is a dustry and academia alike, resulting in a framework that tackles the problem of interoper- large volume of UIMA-compliant process- ability of processing components. Originally de- ing components. In this paper, we demon- veloped by IBM, it is currently an Apache Soft- strate Argo, a Web-based workbench for ware Foundation open-source project1 that is also the development and processing of NLP registered at the Organization for the Advance- pipelines/workflows.
    [Show full text]
  • Three Bionlp Tools Powered by a Biological Lexicon
    Three BioNLP Tools Powered by a Biological Lexicon Yutaka Sasaki 1 Paul Thompson 1 John McNaught 1, 2 Sophia Ananiadou 1, 2 1 School of Computer Science, University of Manchester 2 National Centre for Text Mining MIB, 131 Princess Street, Manchester, M1 7DN, United Kingdom {Yutaka.Sasaki,Paul.Thompson,John.McNaught,Sophia.Ananiadou}@manchester.ac.uk its focus is on medical terms. Therefore some Abstract biology-specific terms, e.g., molecular biology terms, are not the main target of the lexicon. In this paper, we demonstrate three NLP In response to this, we have constructed the applications of the BioLexicon, which is a BioLexicon (Sasaki et al ., 2008), a lexical lexical resource tailored to the biology resource tailored to the biology domain. We will domain. The applications consist of a demonstrate three applications of the BioLexicon, dictionary-based POS tagger, a syntactic in order to illustrate the utility of the lexicon parser, and query processing for biomedical information retrieval. Biological within the biomedical NLP field. terminology is a major barrier to the The three applications are: accurate processing of literature within biology domain. In order to address this • BLTagger: a dictionary-based POS tagger problem, we have constructed the based on the BioLexicon BioLexicon using both manual and semi- • Enju full parser enriched by the automatic methods. We demonstrate the BioLexicon utility of the biology-oriented lexicon • Lexicon-based query processing for within three separate NLP applications. information retrieval 1 Introduction 2. Summary of the BioLexicon Processing of biomedical text can frequently be In this section, we provide a summary of the problematic, due to the huge number of technical BioLexicon (Sasaki et al ., 2008).
    [Show full text]
  • List of Reviewers
    List of Reviewers Area Chairs Key-Sun Choi Korea Advanced Institute of Science and Technology, Korea Machine Translation and Multilinguality Walter Daelmans UniversityofAntwerp, Belgium Machine Learning and Statistical Methods for Syntax Dan Jurafsky University of Colorado, USA Speech, Systems and Evaluation Yuji Matsumoto Nara Institute of Science and Technology, Japan Syntax, Grammars and Morphology Johanna D. Mo ore University of Edinburgh, UK Discourse, Dialogue and Generation Martha S. Palmer UniversityofPennsylvania, USA Lexicon and Semantics Ellen Rilo University of Utah, USA Corpus-based and Statistical Natural Langauge Processing Giorgio Satta UniversityofPadua, Italy Parsing Algorithms and Models Theme Chairs Sanda M. Harabagiu Southern Metho dist University, USA NLP and Open-Domain Question Answering from Text Antal van den Bosch ILK, Tilburg University, The Netherlands Emiel Krahmer IPO, Eindhoven Technical University, The Netherlands Maria Wolters IKP, University of Bonn, Germany Machine Learning and Statistical NLP for Dialogue Inderjeet Mani The Mitre Corp oration Text Summarization Hitoshi Isahara Communications Research Lab oratory, Japan Ra jeev Sangal Indian Institute of Information Technology, India Ming Zhou Microsoft Research, China Theoretical and Technical Approaches for Asian Language Processing: Similarities and Di erences among Languages Reviewers David Aha Naval Research Lab oratory, USA, Sophia Ananiadou University of Salford, UK, Chinatsu Aone SRA, USA Srinivas Bangalore AT&T Research, USA, Tilman Becker
    [Show full text]
  • Towards the French Biomedical Ontology Enrichment
    Awarded by University of Montpellier Prepared at I2S∗ Graduate School, UMR 5506 Research Unit, ADVANSE Team, and Laboratory of Informatics, Robotics And Microelectronics of Montpellier Speciality: Computer Science Defended by Mr Juan Antonio LOSSIO-VENTURA [email protected] Towards the French Biomedical Ontology Enrichment Defended on 09/11/2015 in front of a jury composed by: Sophia Ananiadou Professor Univ. of Manchester President Fabio Crestani Professor Univ. of Lugano Reviewer Pierre Zweigenbaum Professor LIMSI-CNRS Reviewer Natalia Grabar Researcher CNRS Examinator Mathieu Roche Research Professor Cirad, TETIS, LIRMM Director Clement Jonquet Associate Professor Univ. of Montpellier Advisor Maguelonne Teisseire Research Professor TETIS, LIRMM Advisor [<Dreams have only one owner at a time. For that, dreamers tend to be alone.] ∗ I2S: Information, Structures and Systems. 2 Dedication This thesis is lovingly dedicated to my mother Laly Ventura. The fact of making me stronger, her support, encouragement, and constant love have positively influenced all my life. i ii Acknowledgments During the past three years, I met a lot of wonderful people. They helped me with- out asking any response. These people contributed to this thesis as well as to my personal development. First, I would like to thank my thesis committee: Prof. Sophia Ananiadou, Prof. Fabio Crestani, Prof. Pierre Zweigenbaum, and Dr. Natalia Grabar, for their time reading deeply my thesis, for their insightful comments and for the hard questions which allowed me to improve my research from various perspectives. I also would like to express my sincere gratitude to my advisors Clement Jon- quet, Mathieu Roche, and Maguelonne Teisseire for the continuous support of my PhD and related research.
    [Show full text]
  • About the Contributors
    About the Contributors Violaine Prince is full professor at the University Montpellier 2 (Montpellier, France). She obtained her PhD in 1986 at the University of Paris VII, and her ‘habilitation’ (post-PhD degree) at the Univer- sity of Paris XI (Orsay). Previous head of Computer Science department at the Faculty of Sciences in Montpellier, previous head of the National University Council for Computer Science (grouping 3,000 professors and assistant professors in Computer Science in France), she now leads the NLP research team at LIRMM (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier, a CNRS research unit). Her research interests are in natural language processing (NLP) and cognitive science. She has published more than 70 reviewed papers in books, journals and conferences, authored 10 research and education books, founded and chaired several conferences and belonged to program committees as well as journals reading committees. She is member of the board of the IEEE Computer Society French Chapter. Mathieu Roche is assistant professor at the University Montpellier 2, France. He received a PhD in computer science at the University Paris XI (Orsay) in 2004. With Jérôme Azé, he created in 2005 the DEFT challenge (‘DEfi Francophone de Fouille de Textes’ meaning ‘Text Mining Challenge’) which is a francophone equivalent of the TREC Conferences. His current main research interests at LIRMM (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier, a CNRS research unit) are text mining, information retrieval, terminology, and natural language processing for schema mapping. * * * Sophia Ananiadou is director of The National Centre for Text Mining (NaCTeM)providing text mining services with particular focus on biomedicine.
    [Show full text]